2012/01/13 by Tom Britton, Pieter Trapman, Britton, Tom +1
Medicine · Physics and Astronomy · #Complex Network Analysis Techniques #FOS: Computer and information sciences #FOS: Mathematics #FOS: Physical sciences #Mathematical and Theoretical Epidemiology and Ecology Models #Opinion Dynamics and Social Influence #Physics and Society (physics.soc-ph) #Probability (math.PR) #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.1201.2788
openalex publication_date 2012/01/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Social networks are rarely observed in full detail. In many situations\nproperties are known for only a sample of the individuals in the network and it\nis desirable to induce global properties of the full social network from this\n"egocentric" network data. In the current paper we study a few different types\nof egocentric data, and show what global network properties are consistent with\nthose egocentric data. Two global network properties are considered: the size\nof the largest connected component in the network (the giant), and secondly,\nthe possible size of an epidemic outbreak taking place on the network, in which\ntransmission occurs only between network neighbours, and with probability p.\nThe main conclusion is that in most cases, egocentric data allow for a large\nrange of possible sizes of the giant and the outbreak. However, there is an\nupper bound for the latter. For the case that the network is selected uniformly\namong networks with prescribed egocentric data (satisfying some conditions),\nthe asymptotic size of the giant and the outbreak is characterised.\n